A Single Customer View in a Data-Heavy World

The desire to implement a "single customer view" (SCV) is no longer a vision for traditional marketers and data scientists, it is a growing necessity.

Similarly, an explosion in customer touchpoints and digital accessibility has highlighted shortfalls in omnichannel reporting. Namely, channels may be better understood, but they are still interpreted individually and not weaved into larger, more holistic views of customers’ movements through devices and places. Now more than ever, businesses must overcome organizational inertia to ensure customer service, sales and marketing remain functional as touchpoints are augmented.

Before the synthetic overlay of the Internet, marketing teams focused on above-the-line and below-the-line methods which allowed clear definition of mass and targeted communication. The disruption of digital technologies then provided a mesh to blur the boundaries of these touchpoints. However, teams grew disproportionately by channel — some channels proved more profitable than others — and internal investment lopsided company-wide efforts. Only 20 percent of organizations have created a personalized SCV and further only 19 percent of organizations have technology and processes to predict customer behavior in their pursuit of digital/data maturity.

This is often particularly evident when examining a large organization with millions of customers; legacy poses the biggest hindrance to innovation, as stakeholders are skeptical — if is not broken, why fix it? Yet group decisions can stymie the actions of subsidiaries. Where decisions are increasingly requiring escalated sign-off from stakeholders up to board level, ascending layers will muffle the individual needs of each channel.

A simple yes-or-no decision to funnel investment into one channel or consolidate skills in one department will always miss many moving parts and be unable to capture all internal needs. A hierarchical pursuit of SCV will fork decisions if the correct data architecture is unavailable to provide aggregate viewpoints of marketing communications.

A Successful Data Architecture to Yield a Single Customer View

Inclusion should be at the very heart of successful data architecture, regardless of the scale and complexity. Individual players, team leaders and major stakeholders must recognize that every drop of data has value. It is often the case that only the best examples and reports are disseminated through other teams. New insights need affirmation from historical trends and patterns to provide necessary context.

However, the data cannot be housed indiscriminately. To navigate the ocean of documents, a centralized repository — a data lake — is vital. This unstructured database (like NoSQL) swallows all of the data it’s fed and prepares it's accessibility for analytical purposes down the line. Channel-specific teams may not necessarily know the best use for the data now, but it may reveal unknown insights at a later date, generating data discovery. Put simply, "siloization" of channel-focused structures will melt away as every team has a nucleic hub where they can contribute data and integration.

The Benefits — Sales, Marketing and Customer Service

Marketing, sales and customer service teams will immediately see the benefits of a centralized storehouse for their customer data. A reactive attribution model can be built to optimize and understand how channels complement each other and influence success, and then we can begin to perform data science.

It is possible through experimental design to enact different scenarios with control groups, garnering insight into an average "incrementality percentage" — a comparative measure of separate channels' performance over time. Once you have this guideline you can compare weighted paths to conversion over desired time frames with separately weighted campaigns (i.e. campaigns that focus heavily on broadcast media or on banner advertising).

Should the incrementality differ wildly, you must revisit and recalibrate your model until you understand where spikes in conversions are attributed to individual channels. The ultimate step is to employ your findings to budget planning, testing and plotting the success for future omnichannel projects and campaigns.

Churn and attrition analyses, which are not contextually unfit SaaS (Software as a Service) or DaaS (Desktop as a Service) applications, can begin to take shape for customer propensity forecasting. Without correct holistic data management, programmatic advertisers often have the tendency to overlook contextual data, failing to understand industry-specific customer paths, or contractual or singular business types — "flash-selling" at the wrong time. Knowing when to take a back seat is key, and all of your learnings will solidify with solid data management foundations.

Consolidating the Position of Future Touchpoints

In the realm of future technology and its role in marketing communications, companies will almost certainly need to become more flexible and constantly receptive to customers’ needs and ideas. Current integrations of apps and mobile-centric marketing can appear frustrated and broken, flagging pain-points that clotted during the deployment process. Apps now appear too insular, bloating themselves to manage customer services; customers now demand a panacea in the shape of chatbots and micro-apps.

Through more encompassing data management, real-time or near-real-time data feeds will curate content. Imagine an e-commerce website not simply personalized upon its recommended product offerings but also in its navigation style, page order and accessibility. Or in periods of non-interaction, abandoned-cart-triggered emails become more intuitive with the site structure, informing device-specific interplay.

In the nearer future, nearby field communication and maturing mobile payments technologies will continue to open revenue streams. "Smart billboards" and displays will respond to Beacon and Bluetooth-embedded firmware. Shored up with a strong back-end data lake, front-end messenger bots will be technically able to withstand all funneling of previously uncaptured social data. This aids the digitization of in-store experiences, rectifying the online/offline disconnect and assuaging customers’ privacy concerns with in-situ advertisements recognizing contextual factors.

Removing organizational barriers and negotiating legacy issues will prove no easy feat, but the benefits of a single, unified data lake for a coherent customer view will prove immeasurably useful.

Guy is Director and Co-founder at Profusion. He has 16 years’ experience in data-driven digital marketing, which has led him to work across a wide range of sectors including technology, retail, and finance. Guy is passionate about creating and using cutting-edge techniques to improve digital marketing. This led him to set up Profusion, a data science and intelligence marketing company which Guy co-founded with business partner Russell Parsons in 2011. Before founding Profusion, Guy was Managing Director of email marketing company Mailtrack.